Learning Soft Driving Constraints from Vectorized Scene Embeddings while Imitating Expert Trajectories

• URL: http://arxiv.org/abs/2412.05717v1
• Date: Sat, 07 Dec 2024 18:29:28 GMT
• Title: Learning Soft Driving Constraints from Vectorized Scene Embeddings while Imitating Expert Trajectories
• Authors: Niloufar Saeidi Mobarakeh, Behzad Khamidehi, Chunlin Li, Hamidreza Mirkhani, Fazel Arasteh, Mohammed Elmahgiubi, Weize Zhang, Kasra Rezaee, Pascal Poupart,
• Abstract summary: The primary goal of motion planning is to generate safe and efficient trajectories for vehicles.<n>Traditionally, motion planning models are trained using imitation learning to mimic the behavior of human experts.<n>We propose a method that integrates constraint learning into imitation learning by extracting driving constraints from expert trajectories.
• Score: 16.666811573117613
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The primary goal of motion planning is to generate safe and efficient trajectories for vehicles. Traditionally, motion planning models are trained using imitation learning to mimic the behavior of human experts. However, these models often lack interpretability and fail to provide clear justifications for their decisions. We propose a method that integrates constraint learning into imitation learning by extracting driving constraints from expert trajectories. Our approach utilizes vectorized scene embeddings that capture critical spatial and temporal features, enabling the model to identify and generalize constraints across various driving scenarios. We formulate the constraint learning problem using a maximum entropy model, which scores the motion planner's trajectories based on their similarity to the expert trajectory. By separating the scoring process into distinct reward and constraint streams, we improve both the interpretability of the planner's behavior and its attention to relevant scene components. Unlike existing constraint learning methods that rely on simulators and are typically embedded in reinforcement learning (RL) or inverse reinforcement learning (IRL) frameworks, our method operates without simulators, making it applicable to a wider range of datasets and real-world scenarios. Experimental results on the InD and TrafficJams datasets demonstrate that incorporating driving constraints enhances model interpretability and improves closed-loop performance.

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